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Issuance effects are regarded as one of the most important aspects referring to the regulatory guidelines of green corporate bond ratings. This paper developed a new incentive difference Hotelling model, considering four major factors, i.e., the direct effect of issuance, the indirect effect of issuance, the reputation of rating agencies and the regulatory penalties. In this model, how the direct effect and the indirect effect impact the dual rating mechanism and the integrated rating mechanism was discussed. Numerical experiments were conducted to explore the regulatory effects on the two defined mechanisms in different situations. The results demonstrate that under each mechanism, the direct and indirect effects of issuance indirectly improve the effectiveness and efficiency of regulation by increasing the environmental benefit information content in the rating information, and the indirect effect has a greater impact. Moreover, it provides specific recommendations for the design of a regulatory regime.
Hanyi Zhao; Yixiang Tian; Xiangyun Zhou; Luping Zhang; Wei Meng. Rating Regulatory Mechanism Effect Promotion under the Environmental Issuance Effects: Based on the Incentive Difference Hotelling Model. Sustainability 2021, 13, 5368 .
AMA StyleHanyi Zhao, Yixiang Tian, Xiangyun Zhou, Luping Zhang, Wei Meng. Rating Regulatory Mechanism Effect Promotion under the Environmental Issuance Effects: Based on the Incentive Difference Hotelling Model. Sustainability. 2021; 13 (10):5368.
Chicago/Turabian StyleHanyi Zhao; Yixiang Tian; Xiangyun Zhou; Luping Zhang; Wei Meng. 2021. "Rating Regulatory Mechanism Effect Promotion under the Environmental Issuance Effects: Based on the Incentive Difference Hotelling Model." Sustainability 13, no. 10: 5368.
Based on the grey prediction model GM(1,1), a novel fractional-order grey prediction model is proposed and its modeling error is systematically studied. In this paper, exponential data sequences are generated for numerical simulation. Via the numerical simulation method, the mean absolute percentage error (MAPE) of the fractional-order GM(1,1) with different values of order and development coefficient is compared to the GM(1,1) and the discrete GM(1,1). The error distribution of the sequences of exponential data is given. The GM(1,1) and the direct modeling GM(1,1) are both special cases of the fractional-order GM(1,1). The conclusion is helpful to further optimize the grey model using fractional-order operators and to expand the applicable bound of GM(1,1).
Wei Meng; Bo Zeng; Shuliang Li. A Novel Fractional-Order Grey Prediction Model and Its Modeling Error Analysis. Information 2019, 10, 167 .
AMA StyleWei Meng, Bo Zeng, Shuliang Li. A Novel Fractional-Order Grey Prediction Model and Its Modeling Error Analysis. Information. 2019; 10 (5):167.
Chicago/Turabian StyleWei Meng; Bo Zeng; Shuliang Li. 2019. "A Novel Fractional-Order Grey Prediction Model and Its Modeling Error Analysis." Information 10, no. 5: 167.
With the rapid development of the Yangtze River economic belt, the amount of waste-sewage water discharged into the Yangtze River basin increases sharply year by year, which has impeded the sustainable development of the Yangtze River basin. The water security along the Yangtze River basin is very important for China, It is something aboutwater security of roughly one-third of China’s population and the sustainable development of the 19 provinces, municipalities and autonomous regions among the Yangtze River basin. Therefore, a scientific prediction of the amount of waste-sewage water discharged into Yangtze River basin has a positive significance on sustainable development of industry belt along with Yangtze River basin. This paper builds the fractional DWSGM(1,1)(DWSGM(1,1) model is short for Discharge amount of Waste Sewage Grey Model for one order equation and one variable) model based on the fractional accumulating generation operator and fractional reducing operator, and calculates the optimal order of “r” by using particle swarm optimization(PSO)algorithm for solving the minimum average relative simulation error. Meanwhile, the simulation performance of DWSGM(1,1)model with the optimal fractional order is tested by comparing the simulation results of grey prediction models with different orders. Finally, the optimal fractional order DWSGM(1,1)grey model is applied to predict the amount of waste-sewage water discharged into the Yangtze River basin, and corresponding countermeasures and suggestions are put forward through analyzing and comparing the prediction results. This paper has positive significance on enriching the fractional order modeling method of the grey system.
Shuliang Li; Wei Meng; Yufeng Xie. Forecasting the Amount of Waste-Sewage Water Discharged into the Yangtze River Basin Based on the Optimal Fractional Order Grey Model. International Journal of Environmental Research and Public Health 2017, 15, 20 .
AMA StyleShuliang Li, Wei Meng, Yufeng Xie. Forecasting the Amount of Waste-Sewage Water Discharged into the Yangtze River Basin Based on the Optimal Fractional Order Grey Model. International Journal of Environmental Research and Public Health. 2017; 15 (1):20.
Chicago/Turabian StyleShuliang Li; Wei Meng; Yufeng Xie. 2017. "Forecasting the Amount of Waste-Sewage Water Discharged into the Yangtze River Basin Based on the Optimal Fractional Order Grey Model." International Journal of Environmental Research and Public Health 15, no. 1: 20.